Performance of Different Crop Models in Simulating Soil Temperature

Abstract

Soil temperature is one of the key factors to be considered in precision agriculture to increase crop production. This study is designed to compare the effectiveness of a land surface model (Noah Multiparameterization (Noah-MP)) against a traditional crop model (Environmental Policy Integrated Climate Model (EPIC)) in estimating soil temperature. A sets of soil temperature estimates, including three different EPIC simulations (i.e., using different parameterizations) and a Noah-MP simulations, is compared to ground-based measurements from across the Central Valley in California, USA, during 2000–2019. The main conclusion is that relying only on one set of model estimates may not be optimal. Furthermore, by combining different model simulations, i.e., by taking the mean of two model simulations to reconstruct a new set of soil temperature estimates, it is possible to improve the performance of the single model in terms of different statistical metrics against the reference ground observations. Containing ratio (CR), Euclidean distance (dist), and correlation co-efficient (R) calculated for the reconstructed mean improved by 52%, 58%, and 10%, respectively, compared to both model estimates. Thus, the reconstructed mean estimates are shown to be more capable of capturing soil temperature variations under different soil characteristics and across different geographical conditions when compared to the parent model simulations.

Optimization of Data-Driven Soil Temperature Forecast—The First Model in Bangladesh

“…Soil temperature influences the physical, chemical, and microbiological processes that take place in soil [1,2]. It significantly influences the operational seasons of water systems and exerts a profound impact on evapotranspiration, plant growth, development, oxygen availability, and health and yield of various grain crops [3][4][5][6]. A further increase in soil temperature in tropical regions would lead to more negative effects, including seedling death, smaller plants, higher water needs, and increased vulnerability to different plant diseases [7].…”

“…The latest Internet of Things (IoT) sensors are being applied to environmental pollution [20,21], water quality [22], and precision agriculture [23][24][25][26][27], so the large-scale implementation of IoT sensors is a potentially solution to improve future soil temperature observation in these economically challenged countries. Although different land surface models can simulate soil temperature well, these models need to be driven by many downscaled atmospheric forcing variables and soil parameters (e.g., soil thermal diffusive) [3]. These inputs are not available for developing countries, and the simulation process of physics-based models is time-consuming and expensive, leading to machine learning approaches playing a more important role in estimating soil temperature.…”